{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "\n",
    "def loadDataSet(fileName):      #general function to parse tab -delimited floats\n",
    "    numFeat = len(open(fileName).readline().split('\\t')) - 1 #get number of fields \n",
    "    dataMat = []; labelMat = []\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        lineArr =[]\n",
    "        curLine = line.strip().split('\\t')\n",
    "        for i in range(numFeat):\n",
    "            lineArr.append(float(curLine[i]))\n",
    "        dataMat.append(lineArr)\n",
    "        labelMat.append(float(curLine[-1]))\n",
    "    return dataMat,labelMat\n",
    "\n",
    "def standRegres(xArr,yArr):\n",
    "    xMat = mat(xArr); yMat = mat(yArr).T\n",
    "    xTx = xMat.T*xMat\n",
    "    if linalg.det(xTx) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    ws = xTx.I * (xMat.T*yMat)\n",
    "    return ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 0.067732], [1.0, 0.42781]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr, yArr = loadDataSet('ex0.txt')\n",
    "xArr[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 3.00774324],\n",
       "        [ 1.69532264]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = standRegres(xArr, yArr)\n",
    "ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 3.12257084],\n",
       "        [ 3.73301922],\n",
       "        [ 4.69582855],\n",
       "        [ 4.25946098],\n",
       "        [ 4.67099547],\n",
       "        [ 3.89977285],\n",
       "        [ 3.65007895],\n",
       "        [ 3.06514517],\n",
       "        [ 3.23286683],\n",
       "        [ 3.24221654],\n",
       "        [ 3.42785945],\n",
       "        [ 4.10677005],\n",
       "        [ 4.24737842],\n",
       "        [ 3.40925159],\n",
       "        [ 4.6518468 ],\n",
       "        [ 4.03763819],\n",
       "        [ 3.61572324],\n",
       "        [ 3.25838991],\n",
       "        [ 4.08905393],\n",
       "        [ 3.39829811],\n",
       "        [ 3.12681762],\n",
       "        [ 3.12159094],\n",
       "        [ 4.57689489],\n",
       "        [ 4.22453225],\n",
       "        [ 3.03380205],\n",
       "        [ 3.575795  ],\n",
       "        [ 3.07638008],\n",
       "        [ 3.36812645],\n",
       "        [ 4.05412689],\n",
       "        [ 3.92524508],\n",
       "        [ 3.08463121],\n",
       "        [ 3.22264573],\n",
       "        [ 3.95116656],\n",
       "        [ 4.53547307],\n",
       "        [ 4.63266931],\n",
       "        [ 3.47321272],\n",
       "        [ 3.67637171],\n",
       "        [ 4.48687834],\n",
       "        [ 3.70271024],\n",
       "        [ 4.54873897],\n",
       "        [ 3.29055527],\n",
       "        [ 4.13515992],\n",
       "        [ 3.4548456 ],\n",
       "        [ 3.39972557],\n",
       "        [ 3.9451346 ],\n",
       "        [ 3.08868473],\n",
       "        [ 3.62780072],\n",
       "        [ 3.84693134],\n",
       "        [ 3.84432733],\n",
       "        [ 4.35061   ],\n",
       "        [ 4.31256527],\n",
       "        [ 3.43466109],\n",
       "        [ 3.31620889],\n",
       "        [ 4.37786401],\n",
       "        [ 3.57417766],\n",
       "        [ 3.58183035],\n",
       "        [ 3.77501915],\n",
       "        [ 4.18560255],\n",
       "        [ 4.00951278],\n",
       "        [ 3.52978055],\n",
       "        [ 3.25926809],\n",
       "        [ 4.1981649 ],\n",
       "        [ 4.23099312],\n",
       "        [ 4.13833018],\n",
       "        [ 3.2195416 ],\n",
       "        [ 4.0559375 ],\n",
       "        [ 3.65380697],\n",
       "        [ 3.66153255],\n",
       "        [ 4.32530562],\n",
       "        [ 3.20455155],\n",
       "        [ 3.20353097],\n",
       "        [ 4.13260677],\n",
       "        [ 3.43985386],\n",
       "        [ 4.69270407],\n",
       "        [ 3.50859919],\n",
       "        [ 4.62257705],\n",
       "        [ 3.52828697],\n",
       "        [ 3.47779009],\n",
       "        [ 3.48090949],\n",
       "        [ 3.30565212],\n",
       "        [ 3.27286288],\n",
       "        [ 4.25069447],\n",
       "        [ 4.44561758],\n",
       "        [ 4.31514894],\n",
       "        [ 3.73587075],\n",
       "        [ 3.28268389],\n",
       "        [ 4.56246261],\n",
       "        [ 3.54184447],\n",
       "        [ 3.34930498],\n",
       "        [ 3.51131849],\n",
       "        [ 3.57846852],\n",
       "        [ 4.13701291],\n",
       "        [ 3.99762179],\n",
       "        [ 3.15189822],\n",
       "        [ 4.17244007],\n",
       "        [ 4.61771487],\n",
       "        [ 3.32970536],\n",
       "        [ 4.43864133],\n",
       "        [ 3.57277394],\n",
       "        [ 3.73170535],\n",
       "        [ 3.79838917],\n",
       "        [ 3.94128791],\n",
       "        [ 3.47940404],\n",
       "        [ 4.56535313],\n",
       "        [ 4.51074001],\n",
       "        [ 3.97320914],\n",
       "        [ 3.12027706],\n",
       "        [ 3.57694952],\n",
       "        [ 3.73141375],\n",
       "        [ 3.6786604 ],\n",
       "        [ 4.18467182],\n",
       "        [ 3.47015945],\n",
       "        [ 3.86132972],\n",
       "        [ 3.1215316 ],\n",
       "        [ 3.07271818],\n",
       "        [ 4.02344833],\n",
       "        [ 4.61330533],\n",
       "        [ 4.14577773],\n",
       "        [ 3.74488818],\n",
       "        [ 3.87130839],\n",
       "        [ 4.27828923],\n",
       "        [ 3.10609569],\n",
       "        [ 3.82654678],\n",
       "        [ 3.79907916],\n",
       "        [ 3.61427374],\n",
       "        [ 4.61681295],\n",
       "        [ 3.71523529],\n",
       "        [ 4.56822162],\n",
       "        [ 3.27294765],\n",
       "        [ 4.1209972 ],\n",
       "        [ 4.05277064],\n",
       "        [ 4.45457905],\n",
       "        [ 3.23372127],\n",
       "        [ 4.1826137 ],\n",
       "        [ 3.31027187],\n",
       "        [ 3.34600419],\n",
       "        [ 3.13188155],\n",
       "        [ 3.49347861],\n",
       "        [ 3.31633604],\n",
       "        [ 4.06142526],\n",
       "        [ 3.5919243 ],\n",
       "        [ 3.68342425],\n",
       "        [ 3.82212368],\n",
       "        [ 3.26772097],\n",
       "        [ 3.29907766],\n",
       "        [ 4.47772699],\n",
       "        [ 3.38724969],\n",
       "        [ 3.90338389],\n",
       "        [ 4.5169059 ],\n",
       "        [ 3.1880442 ],\n",
       "        [ 4.56385616],\n",
       "        [ 3.03292726],\n",
       "        [ 3.97014739],\n",
       "        [ 3.12447299],\n",
       "        [ 4.03092979],\n",
       "        [ 3.90973965],\n",
       "        [ 4.11201199],\n",
       "        [ 4.53561039],\n",
       "        [ 3.35416039],\n",
       "        [ 4.18782512],\n",
       "        [ 4.64190543],\n",
       "        [ 4.67151593],\n",
       "        [ 4.68257113],\n",
       "        [ 4.68771813],\n",
       "        [ 4.25553631],\n",
       "        [ 3.43763299],\n",
       "        [ 4.15161473],\n",
       "        [ 4.60027508],\n",
       "        [ 3.4068544 ],\n",
       "        [ 3.195112  ],\n",
       "        [ 3.37736257],\n",
       "        [ 4.48229758],\n",
       "        [ 3.3414336 ],\n",
       "        [ 4.6321641 ],\n",
       "        [ 4.6567378 ],\n",
       "        [ 4.27210978],\n",
       "        [ 3.76330447],\n",
       "        [ 3.80548918],\n",
       "        [ 3.93887547],\n",
       "        [ 3.5768478 ],\n",
       "        [ 4.5076613 ],\n",
       "        [ 4.56560234],\n",
       "        [ 3.75676222],\n",
       "        [ 3.20541447],\n",
       "        [ 3.61682859],\n",
       "        [ 4.38911078],\n",
       "        [ 3.66404502],\n",
       "        [ 4.67296204],\n",
       "        [ 4.33158679],\n",
       "        [ 4.11405146],\n",
       "        [ 4.4827248 ],\n",
       "        [ 4.03299809],\n",
       "        [ 3.44040992],\n",
       "        [ 4.24625442],\n",
       "        [ 3.84494273],\n",
       "        [ 3.44346998],\n",
       "        [ 4.42119307],\n",
       "        [ 3.12657688],\n",
       "        [ 3.90129695],\n",
       "        [ 3.20467701]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xMat = mat(xArr)\n",
    "yMat = mat(yArr)\n",
    "yHat = xMat * ws\n",
    "yHat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1112084a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1111c0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(xMat[:, 1].flatten().A[0], yMat.T[:, 0].flatten().A[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.98647356],\n",
       "       [ 0.98647356,  1.        ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrcoef(yHat.T, yMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lwlr(testPoint,xArr,yArr,k=1.0):\n",
    "    xMat = mat(xArr); yMat = mat(yArr).T\n",
    "    m = shape(xMat)[0]\n",
    "    weights = mat(eye((m)))\n",
    "    for j in range(m):                      #next 2 lines create weights matrix\n",
    "        diffMat = testPoint - xMat[j,:]     #\n",
    "        weights[j,j] = exp(diffMat*diffMat.T/(-2.0*k**2))\n",
    "    xTx = xMat.T * (weights * xMat)\n",
    "    if linalg.det(xTx) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    ws = xTx.I * (xMat.T * (weights * yMat))\n",
    "    return testPoint * ws\n",
    "\n",
    "def lwlrTest(testArr,xArr,yArr,k=1.0):  #loops over all the data points and applies lwlr to each one\n",
    "    m = shape(testArr)[0]\n",
    "    yHat = zeros(m)\n",
    "    for i in range(m):\n",
    "        yHat[i] = lwlr(testArr[i],xArr,yArr,k)\n",
    "    return yHat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.176513"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr, yArr = loadDataSet('ex0.txt')\n",
    "yArr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 3.12204471]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lwlr(xArr[0], xArr, yArr, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def showLwlrPlot(xArr, yArr, k=1.0):\n",
    "    yHat = lwlrTest(xArr, xArr, yArr, k)\n",
    "    xMat = mat(xArr)\n",
    "    srtInd = xMat[:, 1].argsort(0)\n",
    "    xSort = xMat[srtInd][:, 0, :]\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(xSort[:, 1], yHat[srtInd])\n",
    "    ax.scatter(xMat[:,1].flatten().A[0], mat(yArr).T.flatten().A[0], s=2, c='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112df9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showLwlrPlot(xArr, yArr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111880fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showLwlrPlot(xArr, yArr, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119bdeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showLwlrPlot(xArr, yArr, 0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rssError(yArr,yHatArr): #yArr and yHatArr both need to be arrays\n",
    "    return ((yArr-yHatArr)**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "abX, abY = loadDataSet('abalone.txt')\n",
    "yHat01 = lwlrTest(abX[0:99], abX[0:99], abY[0:99], 0.1)\n",
    "yHat1 = lwlrTest(abX[0:99], abX[0:99], abY[0:99], 1)\n",
    "yHat10 = lwlrTest(abX[0:99], abX[0:99], abY[0:99], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.785126294649586"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rssError(abY[0:99], yHat01.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "429.89056187009299"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rssError(abY[0:99], yHat1.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549.11817088263888"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rssError(abY[0:99], yHat10.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29783.857753615648"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat01 = lwlrTest(abX[100:199], abX[0:99], abY[0:99], 0.1)\n",
    "rssError(abY[100:199], yHat01.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "573.52614418961343"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat1 = lwlrTest(abX[100:199], abX[0:99], abY[0:99], 1)\n",
    "rssError(abY[100:199], yHat1.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "517.57119053835424"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat10 = lwlrTest(abX[100:199], abX[0:99], abY[0:99], 10)\n",
    "rssError(abY[100:199], yHat10.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518.63631532408499"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = standRegres(abX[0:99], abY[0:99])\n",
    "yHat = mat(abX[100:199]) * ws\n",
    "rssError(abY[100:199], yHat.T.A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ridgeRegres(xMat,yMat,lam=0.2):\n",
    "    xTx = xMat.T*xMat\n",
    "    denom = xTx + eye(shape(xMat)[1])*lam\n",
    "    if linalg.det(denom) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    ws = denom.I * (xMat.T*yMat)\n",
    "    return ws\n",
    "    \n",
    "def ridgeTest(xArr,yArr):\n",
    "    xMat = mat(xArr); yMat=mat(yArr).T\n",
    "    yMean = mean(yMat,0)\n",
    "    yMat = yMat - yMean     #to eliminate X0 take mean off of Y\n",
    "    #regularize X's\n",
    "    xMeans = mean(xMat,0)   #calc mean then subtract it off\n",
    "    xVar = var(xMat,0)      #calc variance of Xi then divide by it\n",
    "    xMat = (xMat - xMeans)/xVar\n",
    "    numTestPts = 30\n",
    "    wMat = zeros((numTestPts,shape(xMat)[1]))\n",
    "    for i in range(numTestPts):\n",
    "        ws = ridgeRegres(xMat,yMat,exp(i-10))\n",
    "        wMat[i,:]=ws.T\n",
    "    return wMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "abX, abY = loadDataSet('abalone.txt')\n",
    "ridgeWeights = ridgeTest(abX, abY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dadef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(ridgeWeights)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regularize(xMat):#regularize by columns\n",
    "    inMat = xMat.copy()\n",
    "    inMeans = mean(inMat,0)   #calc mean then subtract it off\n",
    "    inVar = var(inMat,0)      #calc variance of Xi then divide by it\n",
    "    inMat = (inMat - inMeans)/inVar\n",
    "    return inMat\n",
    "\n",
    "def stageWise(xArr,yArr,eps=0.01,numIt=100):\n",
    "    xMat = mat(xArr); yMat=mat(yArr).T\n",
    "    yMean = mean(yMat,0)\n",
    "    yMat = yMat - yMean     #can also regularize ys but will get smaller coef\n",
    "    xMat = regularize(xMat)\n",
    "    m,n=shape(xMat)\n",
    "    returnMat = zeros((numIt,n)) #testing code remove\n",
    "    ws = zeros((n,1)); wsTest = ws.copy(); wsMax = ws.copy()\n",
    "    for i in range(numIt):\n",
    "        print(ws.T)\n",
    "        lowestError = inf; \n",
    "        for j in range(n):\n",
    "            for sign in [-1,1]:\n",
    "                wsTest = ws.copy()\n",
    "                wsTest[j] += eps*sign\n",
    "                yTest = xMat*wsTest\n",
    "                rssE = rssError(yMat.A,yTest.A)\n",
    "                if rssE < lowestError:\n",
    "                    lowestError = rssE\n",
    "                    wsMax = wsTest\n",
    "        ws = wsMax.copy()\n",
    "        returnMat[i,:]=ws.T\n",
    "    return returnMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
      "text/plain": [
       "array([[ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       ..., \n",
       "       [ 0.05,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36],\n",
       "       [ 0.04,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36],\n",
       "       [ 0.05,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr, yArr = loadDataSet('abalone.txt')\n",
    "stageWise(xArr, yArr, 0.01, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
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      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       ..., \n",
       "       [ 0.043, -0.011,  0.12 , ..., -0.963, -0.105,  0.187],\n",
       "       [ 0.044, -0.011,  0.12 , ..., -0.963, -0.105,  0.187],\n",
       "       [ 0.043, -0.011,  0.12 , ..., -0.963, -0.105,  0.187]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stageWise(xArr, yArr, 0.001, 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 0.0430442 , -0.02274163,  0.13214087,  0.02075182,  2.22403814,\n",
       "         -0.99895312, -0.11725427,  0.16622915]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xMat = mat(xArr)\n",
    "yMat = mat(yArr).T\n",
    "xMat = regularize(xMat)\n",
    "yM = mean(yMat, 0)\n",
    "yMat = yMat - yM\n",
    "weights = standRegres(xMat, yMat.T)\n",
    "weights.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
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      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.045  0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n",
      "[[ 0.04   0.     0.105  0.025  1.295 -0.83  -0.06   0.26 ]]\n"
     ]
    }
   ],
   "source": [
    "returnMat = stageWise(xArr, yArr, 0.005, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c17128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(returnMat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "import json\n",
    "import urllib.request\n",
    "def searchForSet(retX, retY, setNum, yr, numPce, origPrc):\n",
    "    sleep(10)\n",
    "    myAPIstr = 'AIzaSyD2cR2KFyx12hXu6PFU-wrWot3NXvko8vY'\n",
    "    searchURL = 'https://www.googleapis.com/shopping/search/v1/public/products?key=%s&country=US&q=lego+%d&alt=json' % (myAPIstr, setNum)\n",
    "    print(searchURL)\n",
    "    pg = urllib.request.urlopen(searchURL)\n",
    "    retDict = json.loads(pg.read())\n",
    "    for i in range(len(retDict['items'])):\n",
    "        try:\n",
    "            currItem = retDict['items'][i]\n",
    "            if currItem['product']['condition'] == 'new':\n",
    "                newFlag = 1\n",
    "            else: newFlag = 0\n",
    "            listOfInv = currItem['product']['inventories']\n",
    "            for item in listOfInv:\n",
    "                sellingPrice = item['price']\n",
    "                if  sellingPrice > origPrc * 0.5:\n",
    "                    print(\"%d\\t%d\\t%d\\t%f\\t%f\" % (yr,numPce,newFlag,origPrc, sellingPrice))\n",
    "                    retX.append([yr, numPce, newFlag, origPrc])\n",
    "                    retY.append(sellingPrice)\n",
    "        except: print('problem with item %d' % i)\n",
    "    \n",
    "def setDataCollect(retX, retY):\n",
    "    searchForSet(retX, retY, 8288, 2006, 800, 49.99)\n",
    "    searchForSet(retX, retY, 10030, 2002, 3096, 269.99)\n",
    "    searchForSet(retX, retY, 10179, 2007, 5195, 499.99)\n",
    "    searchForSet(retX, retY, 10181, 2007, 3428, 199.99)\n",
    "    searchForSet(retX, retY, 10189, 2008, 5922, 299.99)\n",
    "    searchForSet(retX, retY, 10196, 2009, 3263, 249.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgX = []; lgY = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setDataCollect(lgX, lgY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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